# G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR

arXiv: https://arxiv.org/abs/1910.12612 · HTML: https://mlsanity.com/paper/1910.12612/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [End-to-End Speech Recognition Contextualization with Large Language Models](https://mlsanity.com/paper/2309.10917/) (2023)
- [Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model](https://mlsanity.com/paper/2209.01250/) (2022)
- [A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems](https://mlsanity.com/paper/2108.07493/) (2021)
- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization](https://mlsanity.com/paper/2310.09988/) (2023)
- [Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices](https://mlsanity.com/paper/2409.16469/) (2024)
- [Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function](https://mlsanity.com/paper/2509.09197/) (2025)
- [Federated Heterogeneous Language Model Optimization for Hybrid Automatic Speech Recognition](https://mlsanity.com/paper/2603.04945/) (2026)
- [Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting](https://mlsanity.com/paper/2210.09510/) (2022)
- [Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition](https://mlsanity.com/paper/2509.09196/) (2025)

## Abstract (arXiv)

Grapheme-based acoustic modeling has recently been shown to outperform phoneme-based approaches in both hybrid and end-to-end automatic speech recognition (ASR), even on non-phonemic languages like English. However, graphemic ASR still has problems with rare long-tail words that do not follow the standard spelling conventions seen in training, such as entity names. In this work, we present a novel method to train a statistical grapheme-to-grapheme (G2G) model on text-to-speech data that can rewrite an arbitrary character sequence into more phonetically consistent forms. We show that using G2G to provide alternative pronunciations during decoding reduces Word Error Rate by 3% to 11% relative over a strong graphemic baseline and bridges the gap on rare name recognition with an equivalent phonetic setup. Unlike many previously proposed methods, our method does not require any change to the acoustic model training procedure. This work reaffirms the efficacy of grapheme-based modeling and shows that specialized linguistic knowledge, when available, can be leveraged to improve graphemic ASR.